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Computer Models of Music

The computer has made one of the most profound impacts on the creation, study, and dissemination of music since the invention of the printing press; as its myriad uses have emerged throughout the late 20th and into the 21st century, the computer has surpassed other forms of music technology. Not only is it used for the performance and composition of music but it also serves as a remarkable tool for modeling music for research in the social sciences. Researchers in computer science, music informatics, music theory and systematic musicology, psychology and other social sciences, and artificial intelligence have utilized computer models of music to represent, simulate, and experiment with various aspects of music.

Computer models of music have been developed in recent years for research in a number of areas, namely, those in music perception and cognition, music creativity and composition, and music performance. Projects include data acquisition and automatic structural analysis of music, strategies to examine the basic percepts related to musical expression and expressive performances, inductive models and machine learning, style recognition, classification, and replication.

Computational Models

Modeling is a facet of computer science wherein computational resources are used to synthetically create simulations of real-world events or actions, natural conditions, complex systems, or other situations. Well-known examples of computational models include weather forecasting models and those used in flight or medical simulations. The term computer simulation is often used interchangeably with the term computer model, but as a matter of semantics, it should be noted that “computer model” refers to the algorithms used to create a simulation; the simulation is created when the program that contains these algorithms is run. The simulation that results from the model operates via principles of mathematics, physics, and computer science to help researchers study the behavior of complex systems. By adjusting variables within the model, researchers can see how these changes affect the outcome in the simulations; by creating a realistic model, researchers can make predictions regarding these behaviors in real-world environments.

Computational models are often used in the social sciences, especially in cases in which the research is dangerous, difficult, or impossible to complete with human subjects; very large amounts of data need to be analyzed; constraints in time, funding, materials, test subjects, researchers, etc., come into play; or theoretical models are otherwise impossible to test because of some type of limiting factors. Computer models are also valuable tools used in the visualization or sonification of scientific data.

Computational Models of Music Perception and Cognition

Computational systems have been used for a number of years to formally model theories of music perception and cognition. One of the most common models used to study perception of any kind is an artificial neural network (ANN). ANNs are models of the central nervous system and provide researchers with a system within which they can conduct experiments in pattern recognition, machine learning, and decision making; these networks have been used to create computers and robots capable of playing games such as chess, to make medical diagnoses, to mine for data, and to recognize sequences and patterns. The ability to model pattern recognition has been of particular value to music psychologists. Experiments in music cognition employ ANN models to study pitch perception, pitch, and timbre classification, in order to model goal-directed behaviors in music performance, the perception of temporal sequences, meter, tempo, melodic tendencies, articulation, and even the cognitive processes behind improvisation.

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